CIW AI Data Science Specialist Exam Syllabus

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The CIW AI Data Science Specialist certification is mainly targeted to those candidates who want to build their career in Artificial Intelligence domain. The CIW AI Data Science Specialist exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of CIW AI Data Science Specialist.

CIW AI Data Science Specialist Exam Summary:

Exam Name CIW AI Data Science Specialist
Exam Code 1D0-184
Exam Price $175 (USD)
Duration 90 mins
Number of Questions 54
Passing Score 74.07%
Schedule Exam PSI Store
CIW Shop
Sample Questions CIW AI Data Science Specialist Sample Questions
Practice Exam CIW 1D0-184 Certification Practice Exam

CIW 1D0-184 Exam Syllabus Topics:

Topic Details

Domain 1: Data Science Overview

Fundamentals - Define machine learning
- Explain data science applications for business
- Distinguish the difference between AI and data science
- List applications of data science
- Describe what is the purpose of data science?
- Explain what a correlation coefficient is and how it is calculated
Legal, Ethics and Privacy Considerations - Explain societal impact of AI
- Explain the implications of biased predictions by data models
- Apply ethical reasoning in decision making scenarios
- Identify ethical guidelines to be applied in data science
- Discuss web security standards
- Explain data protection security methodologies
- Demonstrate risks associated with data privacy and integrity
- Demonstrate data collection security principles
Career - Apply data evaluation and data modeling for business solutions
- Describe industries in need of data science
- Read scientific articles, conference papers, etc. to identify emerging analytic trends and technologies
- Learn about the latest developments in your professional field

Domain 2: Analysis

Exploratory Data Analysis - Use data mining techniques
- Explain clustering techniques and their use cases
- Conduct exploratory data analysis
- Explain how to capture properties of distributions (mean, variance, skewness, kurtosis)
- Analyze sets of data using descriptive statistical methods
- Construct frequency distributions
Modeling and Visualization Techniques - Create a visualization of one or two variables in order to understand the data better
- Perform feature selection for supervised and unsupervised analysis
- Explain curse of dimensionality
- Explain the difference between model underfitting and overfitting
- Explain the different types of errors made by a predictive model
- Apply dimensionality reduction techniques (e.g., PCA) for data visualization
- Explain the difference between classification and regression
- Identify different performance metrics for classification (accuracy, ROC curve, AUC, F1)
- Analyze data using correlation and linear regression methods
- Describe data analyzing techniques
Statistics - Provide statistical and mathematical solutions
- Explain linear models and generalized linear models
- Explain bias-variance trade off
- Compare and contrast different model evaluation techniques and their pros and cons
- Define causal inference and with which kind of data it can be performed
- Explain importance of checking model assumptions before deciding on final model
- Explain how to detect bias in a model
- Explain how to evaluate success of model fitting
- Describe statistical power and why it is important
- Explain difference between parametric and non-parametric models
- Explain how to decide which performance metrics to use given a prediction problem
- Explain how to create confidence intervals around estimations
- Explain the difference between the frequentist and Bayesian approaches to probability
- Explain the concept of hypothesis testing

Domain 3: Managing Data

General Data Management - Develop data structures and data warehousing solutions
- Explain how to analyze big datasets through distributed systems (e.g., Hadoop, MapReduce)
- Write SQL queries to fetch the data
- List the different stages in the data cycle
- Explain how to maintain a dataset through integration and scrubbing
- Demonstrate data source attributes, benefits and collection strategies
- Explain data selection criteria and procedures
- Describe methods for acquiring data
Querying Databases - Types of databases and query languages
- Query languages strengths and weaknesses
- Indexes and Query efficiency
Data Preparation - Handle categorical variables
- Explain missing value problem and handling strategies
- Explain what outlier is and how an outlier detection process works
- Demonstrate data preprocessing and normalization

Domain 4: Professional Skills

Programming - Explain basic concepts about algorithm design such as computational complexity
- Program in R
- Use matplotlib and/or seaborn to visualize data
- Use Pandas to represent data
- Use common machine learning packages
- Write syntax for an analysis package (e.g., SPSS, SAS, R)
- Program in Python
- Solve statistical problems using programming languages
Conduct Research - Design and conduct surveys, opinion polls, or other instruments to collect data
- Perform an A/B test to decide of treatment effect
- Describe training and testing datasets and their role in analysis and modeling
Consulting - Provide technical support for existing reports, software, databases, dashboards, or other tools
- Advise others on analytical techniques
Communicating Results - Deliver oral or written presentations of the results of modeling and data analysis
- Compile reports, charts, papers, presentations or white papers that describe and interpret findings of analyses
- Prepare data visualizations to communicate complex results to non-statisticians
- Describe how to interpret and report data analysis results
Deploy Models - Maintain and update existing models using fresh data or to make new predictions
- Choose a methodology for deploying machine learning models for applications
- Develop scalable frameworks
- Describe how to scale a data science solution
Problem Identification - Identify problems that can be solved using machine learning models or data analyses
- Identify business problems or management objectives that can be addressed through data analysis
- Identify solutions to problems (staffing, marketing, etc.) using the results of data analysis

To ensure success in CIW AI Data Science Specialist certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for CIW AI Data Science Specialist (1D0-184) exam.

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